|
--- |
|
license: mit |
|
pipeline_tag: image-text-to-text |
|
library_name: transformers |
|
base_model: |
|
- OpenGVLab/InternVL2-1B |
|
base_model_relation: merge |
|
language: |
|
- multilingual |
|
tags: |
|
- internvl |
|
- custom_code |
|
--- |
|
|
|
# Mini-InternVL2-DA-RS |
|
|
|
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[🆕 Blog\]](https://internvl.github.io/blog/) [\[📜 Mini-InternVL\]](https://arxiv.org/abs/2410.16261) [\[📜 InternVL 1.0\]](https://arxiv.org/abs/2312.14238) [\[📜 InternVL 1.5\]](https://arxiv.org/abs/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) |
|
|
|
[\[🗨️ InternVL Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#quick-start) [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/706547971) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/internvl2.0/domain_adaptation.html#data-preparation) |
|
|
|
|
|
 |
|
|
|
## Introduction |
|
|
|
We release the adaptation models for the specific domains: autonomous driving, medical images, and remote sensing. |
|
|
|
These models are built upon Mini-InternVL and fine-tuned using a unified adaptation framework, achieving good performance on tasks in specific domains. |
|
|
|
 |
|
|
|
<table> |
|
<tr> |
|
<th>Model Name</th> |
|
<th>HF Link</th> |
|
<th>Note</th> |
|
</tr> |
|
<tr> |
|
<td>Mini-InternVL2-DA-Drivelm</td> |
|
<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-Drivelm">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-Drivelm">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-Drivelm">🤗4B</a></td> |
|
<td> Adaptation for <a href="https://github.com/OpenDriveLab/DriveLM/tree/main/challenge"> CVPR 2024 Autonomous Driving Challenge </a></td> |
|
</tr> |
|
<tr> |
|
<td>Mini-InternVL2-DA-BDD</td> |
|
<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-BDD">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-BDD">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-BDD">🤗4B</a></td> |
|
<td> Fine-tuning with data constructed by <a href="https://tonyxuqaq.github.io/projects/DriveGPT4/"> DriveGPT4 </a></td> |
|
</tr> |
|
<tr> |
|
<td>Mini-InternVL2-DA-RS</td> |
|
<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-RS">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-RS">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-RS">🤗4B</a></td> |
|
<td> Adaptation for remote sensing domain </td> |
|
</tr> |
|
<tr> |
|
<td>Mini-InternVL2-DA-Medical</td> |
|
<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-Medical">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-Medical">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-Medical">🤗4B</a></td> |
|
<td> Fine-tuning using our <a href="https://huggingface.co/datasets/OpenGVLab/InternVL-Domain-Adaptation-Data/blob/main/train_meta/internvl_1_2_finetune_medical.json">medical data</a>.</td> |
|
</tr> |
|
</table> |
|
|
|
The script for evaluation is in the [document](https://internvl.readthedocs.io/en/latest/internvl2.0/domain_adaptation.html#id3). |
|
|
|
## Training datasets |
|
|
|
- General domain dataset: |
|
|
|
ShareGPT4V, AllSeeingV2, LLaVA-Instruct-ZH, DVQA, ChartQA, AI2D, DocVQA, GeoQA+, SynthDoG-EN |
|
|
|
- Medicalt dataset: |
|
|
|
PMC-OA, MedICaT, Open-i, MedPix, Quilt-1M, RP3D, MIMIC-CXR, Retina Image Bank and others |
|
|
|
## Quick Start |
|
|
|
We provide an example code to run `Mini-InternVL2-1B` using `transformers`. |
|
|
|
> Please use transformers>=4.37.2 to ensure the model works normally. |
|
|
|
|
|
```python |
|
import numpy as np |
|
import torch |
|
import torchvision.transforms as T |
|
from decord import VideoReader, cpu |
|
from PIL import Image |
|
from torchvision.transforms.functional import InterpolationMode |
|
from transformers import AutoModel, AutoTokenizer |
|
|
|
IMAGENET_MEAN = (0.485, 0.456, 0.406) |
|
IMAGENET_STD = (0.229, 0.224, 0.225) |
|
|
|
def build_transform(input_size): |
|
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD |
|
transform = T.Compose([ |
|
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), |
|
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), |
|
T.ToTensor(), |
|
T.Normalize(mean=MEAN, std=STD) |
|
]) |
|
return transform |
|
|
|
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): |
|
best_ratio_diff = float('inf') |
|
best_ratio = (1, 1) |
|
area = width * height |
|
for ratio in target_ratios: |
|
target_aspect_ratio = ratio[0] / ratio[1] |
|
ratio_diff = abs(aspect_ratio - target_aspect_ratio) |
|
if ratio_diff < best_ratio_diff: |
|
best_ratio_diff = ratio_diff |
|
best_ratio = ratio |
|
elif ratio_diff == best_ratio_diff: |
|
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: |
|
best_ratio = ratio |
|
return best_ratio |
|
|
|
def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False): |
|
orig_width, orig_height = image.size |
|
aspect_ratio = orig_width / orig_height |
|
|
|
# calculate the existing image aspect ratio |
|
target_ratios = set( |
|
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if |
|
i * j <= max_num and i * j >= min_num) |
|
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) |
|
|
|
# find the closest aspect ratio to the target |
|
target_aspect_ratio = find_closest_aspect_ratio( |
|
aspect_ratio, target_ratios, orig_width, orig_height, image_size) |
|
|
|
# calculate the target width and height |
|
target_width = image_size * target_aspect_ratio[0] |
|
target_height = image_size * target_aspect_ratio[1] |
|
blocks = target_aspect_ratio[0] * target_aspect_ratio[1] |
|
|
|
# resize the image |
|
resized_img = image.resize((target_width, target_height)) |
|
processed_images = [] |
|
for i in range(blocks): |
|
box = ( |
|
(i % (target_width // image_size)) * image_size, |
|
(i // (target_width // image_size)) * image_size, |
|
((i % (target_width // image_size)) + 1) * image_size, |
|
((i // (target_width // image_size)) + 1) * image_size |
|
) |
|
# split the image |
|
split_img = resized_img.crop(box) |
|
processed_images.append(split_img) |
|
assert len(processed_images) == blocks |
|
if use_thumbnail and len(processed_images) != 1: |
|
thumbnail_img = image.resize((image_size, image_size)) |
|
processed_images.append(thumbnail_img) |
|
return processed_images |
|
|
|
def load_image(image_file, input_size=448, max_num=12): |
|
image = Image.open(image_file).convert('RGB') |
|
transform = build_transform(input_size=input_size) |
|
images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) |
|
pixel_values = [transform(image) for image in images] |
|
pixel_values = torch.stack(pixel_values) |
|
return pixel_values |
|
|
|
# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section. |
|
path = 'OpenGVLab/Mini-InternVL2-1B-DA-Medical' |
|
model = AutoModel.from_pretrained( |
|
path, |
|
torch_dtype=torch.bfloat16, |
|
low_cpu_mem_usage=True, |
|
use_flash_attn=True, |
|
trust_remote_code=True).eval().cuda() |
|
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False) |
|
|
|
# set the max number of tiles in `max_num` |
|
pixel_values = load_image('path/to/image.jpg', max_num=12).to(torch.bfloat16).cuda() |
|
generation_config = dict(max_new_tokens=1024, do_sample=True) |
|
|
|
# pure-text conversation (纯文本对话) |
|
question = 'Hello, who are you?' |
|
response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True) |
|
print(f'User: {question}\nAssistant: {response}') |
|
|
|
question = 'Can you tell me a story?' |
|
response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True) |
|
print(f'User: {question}\nAssistant: {response}') |
|
|
|
# single-image single-round conversation (单图单轮对话) |
|
question = '<image>\nPlease describe the image shortly.' |
|
response = model.chat(tokenizer, pixel_values, question, generation_config) |
|
print(f'User: {question}\nAssistant: {response}') |
|
|
|
# single-image multi-round conversation (单图多轮对话) |
|
question = '<image>\nPlease describe the image in detail.' |
|
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) |
|
print(f'User: {question}\nAssistant: {response}') |
|
|
|
question = 'Please write a poem according to the image.' |
|
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) |
|
print(f'User: {question}\nAssistant: {response}') |
|
|
|
# multi-image multi-round conversation, combined images (多图多轮对话，拼接图像) |
|
pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda() |
|
pixel_values2 = load_image('path/to/image2.jpg', max_num=12).to(torch.bfloat16).cuda() |
|
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) |
|
|
|
question = '<image>\nDescribe the two images in detail.' |
|
response, history = model.chat(tokenizer, pixel_values, question, generation_config, |
|
history=None, return_history=True) |
|
print(f'User: {question}\nAssistant: {response}') |
|
|
|
question = 'What are the similarities and differences between these two images.' |
|
response, history = model.chat(tokenizer, pixel_values, question, generation_config, |
|
history=history, return_history=True) |
|
print(f'User: {question}\nAssistant: {response}') |
|
|
|
# multi-image multi-round conversation, separate images (多图多轮对话，独立图像) |
|
pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda() |
|
pixel_values2 = load_image('path/to/image2.jpg', max_num=12).to(torch.bfloat16).cuda() |
|
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) |
|
num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)] |
|
|
|
question = 'Image-1: <image>\nImage-2: <image>\nDescribe the two images in detail.' |
|
response, history = model.chat(tokenizer, pixel_values, question, generation_config, |
|
num_patches_list=num_patches_list, |
|
history=None, return_history=True) |
|
print(f'User: {question}\nAssistant: {response}') |
|
|
|
question = 'What are the similarities and differences between these two images.' |
|
response, history = model.chat(tokenizer, pixel_values, question, generation_config, |
|
num_patches_list=num_patches_list, |
|
history=history, return_history=True) |
|
print(f'User: {question}\nAssistant: {response}') |
|
|
|
# batch inference, single image per sample (单图批处理) |
|
pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda() |
|
pixel_values2 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda() |
|
num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)] |
|
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) |
|
|
|
questions = ['<image>\nDescribe the image in detail.'] * len(num_patches_list) |
|
responses = model.batch_chat(tokenizer, pixel_values, |
|
num_patches_list=num_patches_list, |
|
questions=questions, |
|
generation_config=generation_config) |
|
for question, response in zip(questions, responses): |
|
print(f'User: {question}\nAssistant: {response}') |
|
|
|
``` |
|
## Citation |
|
|
|
If you find this project useful in your research, please consider citing: |
|
|
|
```BibTeX |
|
@article{gao2024mini, |
|
title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance}, |
|
author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others}, |
|
journal={arXiv preprint arXiv:2410.16261}, |
|
year={2024} |
|
} |
|
@article{chen2024expanding, |
|
title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling}, |
|
author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others}, |
|
journal={arXiv preprint arXiv:2412.05271}, |
|
year={2024} |
|
} |
|
@article{chen2024far, |
|
title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites}, |
|
author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others}, |
|
journal={arXiv preprint arXiv:2404.16821}, |
|
year={2024} |
|
} |
|
@inproceedings{chen2024internvl, |
|
title={Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks}, |
|
author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and others}, |
|
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
|
pages={24185--24198}, |
|
year={2024} |
|
} |
|
``` |
